System and Method for Assessing and Correcting Potential Underserved Content In Natural Language Understanding Applications

ABSTRACT

Methods, systems, and related products that provide detection of media content items that are under-locatable by machine voice-driven retrieval of uttered requests for retrieval of the media items. For a given media item, a resolvability value and/or an utterance resolve frequency is calculated by a number of playbacks of the media item by a speech retrieval modality to a total number of playbacks of the media item regardless of retrieval modality. In some examples, the methods, systems and related products also provide for improvement in the locatability of an under-locatable media item by collecting and/or generating one or more pronunciation aliases for the under-locatable item.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/557,265 filed on Sep. 12, 2017, and U.S. ProvisionalPatent Application No. 62/567,582 filed on Oct. 3, 2017, the contents ofwhich applications are hereby fully incorporated by reference in theirentirety.

TECHNICAL FIELD

The present disclosure relates generally to automatic speech recognitionand natural language understanding, and more particularly to improvingsystems and methods that assess and correct underserved content inautomatic speech recognition and natural language understandingapplications.

BACKGROUND

Voice and natural language understanding applications rely on being ableto understand what a user is saying or typing. Certain types ofcontent/entities are, however, hard to recognize from uttered commands.This includes names containing special characters (emojis, symbols),such as !!! (pronounced ‘chik chik chik’), and other names which usersmay have trouble pronouncing for any of a variety of reasons.

Accessing names of media content items, e.g., by the name of the item orby the name of the artist, can be particularly challenging when the nameof the item or artist sounds like another natural language word or wordsthat is/are spelled differently (i.e., spelled conventionally) and thatconventionally spelled word is associated with a media content itemother than the one that is desired to be accessed. For example, anartist FOCVS goes by the pronunciation “focus”. Uttering the word“focus”, e.g., in the context of “play my focus tracks” for voiceprocessing by a media playback system therefore may not access thedesired content or content by the desired artist, instead retrieving,e.g., content that transcribes exactly to the utterance according toconventional speech to text processing.

Names of tracks or artists that are spelled in unconventional ways orpronounced in unconventional ways, can present additional accessibilitychallenges in that different users may attempt to access the contentusing different utterances. For example, one user may search for theterm MXMUS by uttering “maximus”, while another user utters each letterM-X-M-U-S, and while another user utters “mix music”. Different accentsand dialects of users making utterance-based searches present a similarand/or compounding of the problem.

U.S. Pat. No. 8,959,020 describes methods and systems for discovery ofproblematic pronunciations for automatic speech recognition systems,including determining a frequency of occurrences for one or more n-gramsin transcribed text and a frequency of occurrences of the n-grams intyped text and classifying a system pronunciation of a word included inthe n-grams as correct or incorrect based on the frequencies.

U.S. Pat. Pub. No. 2013/0179170 describes providing validated text-tospeech correction hints from aggregated pronunciation correctionsreceived from text-to-speech applications. Crowd sourcing techniques canbe used to collect corrections to mispronunciations of words or phrasesin text-to-speech applications.

U.S. Pat. Pub. No. 2004/0172258 describes techniques for disambiguatingspeech input for multimodal systems by using a combination of speech andvisual I/O interfaces to correctly identify a user's speech input.

U.S. Pat. Pub. No. 2003/0182111 describes a speech training method. Adigital representation of a particular audible sound is compared to thedigital representations of known audible sounds to determine which ofthose known audible sounds is most likely to be the particular audiblesound being compared to. In response to a determination of errorcorresponding to a known type or instance of mispronunciation, a systempresents an interactive training program from the computer to the userto enable the user to correct such mispronunciation.

U.S. Pat. Pub. No. 2014/0222415 describes training a classifier thatsubsequently determines a degree to which a grapheme-to-phonemealgorithm is likely to detect a newly detected out-of-vocabulary word tobe converted into an audio representation. Outputs are compared toidentify instances in which a lexicon lookup algorithm and agrapheme-to-phoneme algorithm produce different audio representationsfor the same words.

SUMMARY

The present disclosure provides systems, methods, and computer readableproducts for detecting media items that are poorly locatable by machinevoice-driven retrieval to identify the media item, as well as systems,methods, and computer readable products for improving locatability tosuch media items using machine voice-driven retrieval to identify themedia item. The term “locatability” will sometimes be referred to as“locatability” and will sometimes be referred to as “findability”. Thedisclosures herein provide for improvements in retrieving media contentitems using uttered requests by identifying under-locatable content andthen making that content more accessible.

Generally, an “under-locatable” or “underserved” media content item (ormedia item) is one that is deemed to be too difficult or too unlikely tolocate by machine voice-driven retrieval of a syntactically sufficientutterance designed to locate (e.g., in order to playback) that mediacontent item. If a media content item is not under-locatable then it is“locatable,” meaning that it is not too difficult or too unlikely tolocate the media content item by machine voice-driven retrieval of asyntactically sufficient utterance designed to locate (e.g., in order toplayback) that media content item. More specific definitions of whatmakes a media content item under-locatable or locatable will bedescribed below in connection with the methods and systems of thepresent disclosure that identify under-locatable content.

As used herein, a “media content item” includes a single piece of mediacontent (e.g., a track or a television episode or a clip, etc.) or acollection of media content pieces (e.g., an album, a playlist, anentire season or series of episodes of a television show, etc.).

As used herein, the term “syntactically sufficient” means that a commanduttered to locate a media content item, e.g., for purposes of playingback that media content item, meets one or more syntactical requirementsfor causing a locating of the media content item from a repository ofmedia content items to be performed. For example, in some media contentplayback systems, “Play Desert Cactus by the Acmes” triggers a locatingfunction of the system such that the system locates the track DesertCactus by the Acmes and prepares that track for playback, while utteringmerely “Desert Cactus” does not include the necessary syntax to triggerthe desired locating. It should be appreciated that what constitutes asyntactically sufficient utterance can depend on numerous factors.

As used herein a “name entity” or “entity” is a piece of text associatedwith a media content item used to identify that media content item. Itshould be understood that the term “text” is used for convenience andmay refer to, for example, alpha characters, numeric characters,alphanumeric characters, American Standard Code for InformationInterchange (ASCII) characters, symbols, or foreign language unicode(e.g. UTF-8). Thus, a “name entity” includes, e.g., a title of a trackor album, the name of an artist or a band, the title of a playlist, etc.

It should be appreciated that, while the following description will bedirected to locating media content for playback, the principles of thepresent disclosure are applicable more broadly to locating any type ofcontent.

The systems, methods, and computer readable products of the presentdisclosure serve a variety of technical advantages and improvements overexisting technologies and, particularly, over existing computertechnologies directed to locating of media content items and other itemsusing machine voice-driven retrieval and processing of human speech.

According to certain aspects of the present disclosure, a method fordetecting an under-locatable media content item includes: ascertaining afirst number of playbacks of the media content item; ascertaining asecond number of playbacks of the media content item, where each of thesecond number of playbacks corresponds to one of the first number ofplaybacks triggered by a machine voice-driven retrieval of a playbackcommand utterance; and comparing the first number to the second number.

In some examples, each of the first number of playbacks of the mediacontent item corresponds to a playback command provided by any of aplurality of command modalities, where one of the command modalities isuser utterance (i.e., natural speech) and another of the commandmodalities is user-entered text.

In some examples, the method further includes calculating, from thecomparison (i.e., the comparing step), an utterance resolve frequency ofthe media content item; comparing the utterance resolve frequency to apredefined threshold frequency; and, if the utterance resolve frequencyis less than the predefined threshold frequency, determining that themedia content item is under-locatable.

In some examples, the method further includes calculating, from thecomparison (i.e., the comparing step), an utterance resolvability valueof the media content item; comparing the resolvability value to apredefined threshold resolvability; and, if the utterance resolvabilityvalue is less than the predefined threshold resolvability, determiningthat the media content item is under-locatable. In some examples, theresolvability value is subjected to a normalizing function to provide anormalized resolvability value.

In some examples, the media item is associated with one or more nameentities, and the method includes classifying the one or more nameentities with one or more tags; and detecting that the media item isunder-locatable based in part on the one or more tags. In some exampleseach tag indicates that the name entity is one of: Dialectical,Neologism, Non-English, Abbreviation, Ambiguous Acronym, Number, Date,Time, Missing Space, Vocable, Non-Replacement Symbol, Orthographicallyand Semantically Similar Replacement Symbol, Censored, AlternateSpelling, Wordplay, and Proper Noun. In some examples, the one or moretags with which the name entity is classified is used to improve thelocatability of the media content item and/or the locatability of one ormore other media content items.

In some examples, the method further includes crowd-sourcing one or morepronunciation aliases for the name entity or name entities associatedwith the media item; and associating the one or more pronunciationaliases with the media content item. In some examples, subsequent to theassociating the one or more pronunciation aliases with the media contentitem, the method further includes receiving a locating command includingone of the one or more pronunciation aliases; and locating the mediacontent item using one of the one or more pronunciation aliases.

According to further aspects of the present disclosure, a system fordetecting an under-locatable media content item includes one or moreprocessors adapted to: ascertain a first number of playbacks of themedia content item; ascertain a second number of playbacks of the mediacontent item, where each of the second number of playbacks correspondsto one of the first number of playbacks triggered by a machinevoice-driven retrieval of a playback command utterance; and compare thefirst number to the second number.

According to further aspects of the present disclosure, a non-transitorycomputer-readable medium includes one or more sequences of instructionsthat, when executed by one or more processors, causes the one or moreprocessors to detect an under-locatable media content item by:ascertaining a first number of playbacks of the media content item;ascertaining a second number of playbacks of the media content item,where each of the second number of playbacks corresponds to one of thefirst number of playbacks triggered by a machine voice-driven retrievalof a playback command utterance; and comparing the first number to thesecond number.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the present disclosure will become moreapparent from the detailed description set forth below when taken inconjunction with the following drawings. Throughout the several figuresand embodiments, like components are referred to by like referencenumbers unless otherwise indicated.

FIG. 1 schematically illustrates components of an example system inaccordance with the present disclosure.

FIG. 2 schematically illustrates playback records in the playbackhistory storage of the system of FIG. 1.

FIG. 3 schematically illustrates an example task item passed to thealias generation engine of the system of FIG. 1.

FIG. 4 schematically illustrates an embodiment of a look-up table storedon the name entity storage of the system of FIG. 1.

FIG. 5 schematically illustrates an example process flow for the systemof FIG. 1.

FIG. 6 is a block diagram showing an exemplary device constructed torealize one or more aspects of the example embodiments described herein.

DETAILED DESCRIPTION

The example embodiments presented herein are directed to systems,methods, and non-transitory computer-readable medium products thatidentify under-locatable media content items and/or improve thelocatability of under-locatable media content items. The exampleembodiments described are for convenience only, and are not intended tolimit the application of the present invention. After reading thefollowing description, it will be apparent to one skilled in therelevant art how to implement the following disclosure in alternativeembodiments. Such alternative implementations include, for example,systems that store, organize, retrieve, display, or otherwise output anyform of digitalized content, such as documents and websites, as well asmedia content, such as videos, movies, video games, etc., at least inpart by machine recognition of utterances.

FIG. 1 schematically illustrates components of an example system 10 inaccordance with the present disclosure. The various components arecommunicatively coupled, e.g., via a network 2 or through one or morehard electrical and/or electronic connections. The components of thesystem 10 reside in a single location, or across multiple locations. Forexample, all of the components reside in a single user device, such as adesktop or a laptop computer, a smart phone, a tablet, a deviceassociated with a vehicle for providing playback of media content in thevehicle, etc. In another example, the components of the system 10 residein two or more devices, such as a user device, one or more serverspositioned remotely from the user device, and/or one or more databaseslocated remotely from the user device and/or the one or more servers.

The system 10 includes a speech interface 12, a speech-to-text (STT)engine 14, a retrieval engine 16, an alias collection engine 18, analias generation engine 20, a media repository 22, a name entity storage24, a playback history storage 30, and a media playback interface 26. Insome examples, the media playback interface 26 and/or the speechinterface 12 is/are associated with a user device 28; in some examples,they are associated with different user devices. In some examples, theuser device 28 includes one or more other interfaces, such as a displayscreen, a key pad for inputting data to and outputting data provided bythe system 10.

It should be appreciated that the media repository 22 can includemultiple storage devices, e.g., databases, in one or more locations.Similarly, the name entity storage 24 can include multiple storagedevices, e.g., databases, in one or more locations. Similarly, theplayback history storage 30 can include multiple storage devices, e.g.,databases, in one more locations. Alternatively, one or more of themedia repository 22, the name entity storage 24, and/or the playbackhistory storage 30 can correspond to a single storage device, e.g., acentral server database or other memory.

In a typical playback mode of the system 10, a user 4 utters a playbackcommand or request, e.g., “Play Desert Cactus by the Acmes” which iscaptured by the speech interface 12. For example, the speech interface12 includes a microphone, which converts the natural speech into ananalog signal. The analog signal is then converted into a digital signal(using one or more analog to digital converters) which is processed bythe STT Engine 14.

The STT 14 engine transcribes the digital signal corresponding to thecaptured natural speech into text. In a typical transcription process,the STT engine 14 translates the speech signal into sound units calledphonemes, and then maps the phonemes to words using a stored lexicon ofwords. In some examples, the context of the words is also used to inferthe correct transcription. For example, if the phonemes translated from“Desert Cactus” are imprecise or unclear due to poor transmission or anaccent of the user 4, the transcription of “Desert Cactus” by the STTengine 14 may be informed by “by the Acmes” since “Desert Cactus” and“Acmes” often co-occur in a playback utterance. In essence, the STTengine 14 provides a probabilistic transcription based on the availableinformation, and the probability improves as the STT engine 14 learnsfrom experience what words co-occur and at what frequencies. The STTengine 14 can also learn stylizations of specific users. That is, theSTT engine 14 learns how to correctly map phonemes depending on theperson that has uttered them, thereby taking into account users'individual accents, dialects, rhythm, pace, etc. The STT engine 14 canalso learn from feedback, e.g., an input indicating that a particulartranscription was correct or incorrect. Thus, in some examples, the STTengine 14 is adapted to access historical records of transcriptions toimprove the probability of performing an accurate transcription in thepresent. Over time, the STT engine's speech recognition tends to improveat least with respect to words that have conventional spellings andpronunciations.

The transcribed playback request is passed to the retrieval engine 16.Using a retrieval module 40, the retrieval engine 16 maps thetranscribed request to a media item by identifying one or more entitiesin the transcription. For example, if the STT engine 14 provides thetranscribed text “Play Desert Cactus by the Acmes” the retrieval module40 parses the text, identifies the name entity “Desert Cactus,” and thenlooks up the name entity in the name entity storage 24. In someexamples, the name entity storage 24 includes a look-up table 50 havingentries that map each media item stored (e.g., using the media itemidentifier) in the media repository 22 to one or more name entitiesand/or one or more aliases associated with the media item identifier(ID). The retrieval module 40 then passes the returned media item IDassociated with the name entity to the media repository 22, where themedia item associated with the media item ID is identified and thenplayed back, e.g., via the playback interface 26 (e.g., a speaker, adisplay screen, etc.) of the user device 28.

A record of the playback is stored in the playback history storage 30.The playback record includes the ID of the media item played back andthe retrieval modality employed by the user to obtain that particularplayback. Example retrieval modalities include utterance (i.e., naturalspeech) and entered text.

The retrieval engine 16 also includes a retrieval improvement module(RIM) 42. The RIM 42 is configured to ascertain under-locatable nameentities associated with media content items stored in the mediarepository 22. When the RIM 42 is active, the system 10 operates in aretrieval optimization mode. It should be appreciated that the system 10can operate in both retrieval optimization mode and playback mode at thesame time. In some examples, the retrieval module 40 and the RIM 42 areconfigured to operate asynchronously in order to improve the operatingefficiency of the system 10 and reduce overall processing times.

The RIM 42 is triggered by any of a number of triggering events, someexamples of which will now be described. In one example, a user-enteredcommand (entered, e.g., via the user device 28) triggers operation ofthe RIM 42. For instance, a user specifically requests that the RIM 42perform a specific or general locatabilty review. In another example,the RIM 42 is programmed to undergo preset periodic locatability reviewsof name entities associated with media content items stored in the mediarepository 22. In another example, the RIM 42 is triggered by a userfeedback following a media item retrieval (e.g., playback) request. Forexample, a user feedback of “Not the track I wanted” in response to aplayback of a media item ensuing from an uttered playback requesttriggers the RIM 42 to perform a locatability review of the played backmedia content item and/or of the correct media content item, e.g., ifthe user feedback includes a typed entry of the correct media item thatwas desired.

A locatability review to detect whether a media item is under-locatableis performed by the RIM 42 and can be specific to one or more particularmedia items, or more general, e.g., performed across the entirecollection of media items in the repository 22.

For a given review (whether specific or general) the RIM 42 selects afirst media item from the repository 22 by item ID. The RIM 42 theninterrogates the playback history storage 30, and counts the totalnumber of past playbacks of the media item, as well as the number ofpast playbacks of the media item retrieved using the selected nameentity via an utterance retrieval modality.

In at least some examples, the playback records are retrieval specific.That is, the item ID associated with each playback record is the item IDthat corresponds to the scope of the retrieval request that prompted theplayback. Thus, for example, if a track X is part of an album Y, thenthe playback record of album Y (indicating that a user requestedplayback of album Y) will have a different item ID in its playbackrecord than a playback of track X by itself (indicating that a userrequested playback of track X), even though the playback of album Yincludes a playback of the track X. The media item track X may beunder-locatable while the album Y that contains track X may belocatable; thus, it is advantageous to differentiate playback recordsaccording to the scope of the request that caused their playback.

Example playback records 110 stored in the playback history storage 30are schematically depicted in FIG. 2. In non-limiting examples, the dataof the playback records are stored in a table 100. Each playback record110 includes at least three associated components: the item ID 102, atime stamp 104 corresponding to when the item having the item ID 102 wasretrieved for playback, and the retrieval modality 106. In someexamples, the time stamp 104 is applied by a clock 70 (see FIG. 1) ofthe system 10.

When the RIM 42 interrogates the playback history storage 30 to detectan under-locatable media item, each interrogation is specific to oneitem ID. Thus, for example, to detect whether track X isunder-locatable, the RIM 42 interrogates the playback history storage 30for playback records having the track X ID, and does not interrogate theplayback history storage 30 for playback records having the album Y ID.

In some examples, using, e.g., the time stamps 104, the interrogation islimited by a time frame to ensure that the data reflects recentactivity, i.e., playbacks that occurred within a predefined time period,e.g., the past six months. Alternatively, in some examples the playbackhistory storage 30 automatically dumps records that are more than apredefined age, e.g., more than six months old.

Once the total number of playbacks for a given item ID have been counted(e.g., by the counter 72 of the retrieval engine 16), in some examples,the RIM 42 determines if the total number of playbacks meets apredefined minimum threshold number for statistical significance. If thethreshold is not reached, the locatability review for the media item isterminated. If the threshold is reached, the RIM 42 then compares thenumber of past playbacks of the media item retrieved using an utteranceretrieval modality to the total number of past playbacks of the mediaitem regardless of the retrieval modality, in order to measure anutterance resolve frequency.

In some examples, the utterance resolve frequency is a ratio of the rawnumbers of playbacks of the media item retrieved via a speech retrievalmodality to playbacks of the media item regardless of retrievalmodality. In other examples, the utterance resolve frequency is anormalized ratio of the raw numbers of playbacks, where the raw numberratio has been subjected to a predefined normalization function. Using anormalized ratio in some examples reduces the impact of data outlierdata on utterance resolve frequency. In some examples, an utteranceresolve frequency is converted into a resolvability measurement y(“resolvability y”) for a given media item. In one example, a normalizedequation for the resolvability y of a media item is given by equation(1):

$\begin{matrix}{{y = {\log \left( \frac{a}{b + 1} \right)}},} & (1)\end{matrix}$

where a is the total number of playbacks of the media item prompted byplayback requests specific to that media item and regardless ofretrieval modality, and b is the total number of playbacks of the mediaitem prompted by playback requests specific to that media item using anutterance retrieval modality. For a given media item, once y has beencalculated, the RIM 42 compares it to a predefined thresholdresolvability to determine whether the media item is locatable orunder-locatable.

For a given media item, the RIM 42 compares the utterance resolvefrequency (whether normalized or not) or its calculated resolvability yto a predefined threshold frequency or predefined thresholdresolvability, respectively. If the utterance resolve frequency orresolvability y is less than the respective predefined threshold, themedia item is determined by the RIM 42 to be under-locatable. If theutterance resolve frequency or resolvability y meets or exceeds thepredefined threshold frequency or resolvability, the media item isdetermined by the RIM 42 to be locatable.

For a media item determined by the RIM 42 to be under-locatable, themedia item ID is paired by the RIM 42 with its one or more associatedname entities to form a task item that is passed to the alias generationengine 20 for further processing as described below. For example, for agiven media item, the RIM 42 retrieves its one or more name entities forthe name entity storage 24 by looking up the ID of the media item in thetable 50.

In an illustrative example of the capabilities of the RIM 42, the RIM 42interrogates the playback history storage 30 for the unique media IDXXYYY corresponding to the track 2L8 by artist Sir Acme. A mediaidentifier (ID) XXYYY can be comprised of, for example, alphanumericcharacters or other representations that, when processed, link metadatato an associated media content item. The RIM 42 counts (e.g., with thecounter 72) the total number of playback records stored in the playbackhistory storage 30 and having the unique media ID XXYYY, counts thetotal number of playback records stored in the playback history storage30 having the unique media ID XXYYY and an associated utteranceretrieval modality; calculates, using the two counted numbers, aresolvability y, and then compares the calculated resolvability to apredefined threshold resolvability to determine if the media ID XXYYY islocatable or under-locatable. If deemed locatable, then no correctiveaction is taken. If deemed under-locatable, the RIM 42 pairs the mediaitem ID XXYYY with its one or more associated name entities pulled fromthe name entity storage 24 to form a task item that is passed to thealias generation engine 20 for further processing as described below.

In another illustrative example of the capabilities of the RIM 42, theRIM 42 interrogates the playback history storage 30 for the unique mediaID XXYYY corresponding to the track 2L8 by artist Sir Acme. The RIM 42counts (e.g., with the counter 72) the total number of playback recordsstored in the playback history storage 30 and having the unique media IDXXYYY, counts the total number of playback records stored in theplayback history storage 30 having the unique media ID XXYYY and anassociated utterance retrieval modality; calculates, using the twocounted numbers, an utterance resolve frequency, and then compares thecalculated utterance resolve frequency to a predefined thresholdutterance resolve frequency to determine if the media ID XXYYY islocatable or under-locatable. If deemed locatable, then no correctiveaction is taken. If deemed under-locatable, the RIM 42 pairs the mediaitem ID XXYYY with its one or more associated name entities pulled fromthe name entity storage 24 to form a task item that is passed to thealias generation engine 20 for further processing as described below.

In some examples, the RIM classifies the media item ID as somethingother than locatable or under-locatable, such as “questionable.” Aquestionable media item ID is one for which, e.g., the available data isinconclusive as to whether the media item ID is locatable orunder-locatable. This could occur, for example, if there is not astatistically significant number of playback records for the media item,or if the media item was only recently added to the repository 22, or ifthe media item's name entity is tagged as a typically non-problematicclass of name entities despite having a relatively low number ofsuccessful utterance retrieval records. Name entity classes and tagswill be described in greater detail below.

As described, for a media item determined by the RIM 42 to beunder-locatable, the media item ID is paired by the RIM 42 with its oneor more associated name entities from the table 50 to form a task itemthat is passed to the alias generation engine 20.

For example, for a given media item, the RIM 42 retrieves its one ormore name entities from the name entity storage 24 by looking up the IDof the media item in the table 50.

An example task item 120 is schematically illustrated in FIG. 3. Thetask item 120 includes at least two components: the item ID 122 and anyassociated name entities 124 matching the item ID 122. In some examples,the task item 120 also includes a filled or fillable classification cell126. In some examples, the task item 120 also includes a filled orfillable alias cell 128.

Using the track 2L8 by Sir Acme as an example where that content itemhas been determined by the RIM 42 (FIG. 1) to be under-locatable, a taskitem 120 is generated where the item ID 122 corresponds to the contentID of the track 2L8, the name entities cell 124 is populated with thename entity “2L8”, and the classification cell 126 and alias cell 128are unpopulated because no prior aliases or classifications have beengenerated for the name entity “2L8”. This could occur, for example, ifthe track 2L8 is a relatively newly introduced media item to therepository 22 (FIG. 1), and so locatability detection has not beenpreviously performed on this particular media item.

Referring to FIGS. 1 and 3, the RIM 42 passes the task item 120 to thealias generation engine 20, which collects one or more aliases for thename entity of the media item corresponding to the ID 122 using thealias collection engine 18. In at least some examples, the aliascollection engine 18 collects aliases by crowd-sourcing uttered aliasesvia an interface of the user device 28 or any other user devicecommunicatively coupled to the alias collection engine 18, such as theuser devices of a plurality of users of the system 10.

For example, the alias collection engine generates an output thatappears as text on a user interface. The text includes the name entityof the media item (in this case, “2L8” or “2L8 by Sir Acme”) and invitesthe user to utter the name entity, the utterance being received throughthe interface. For example, the text reads: “Say ‘play 2L8 by Sir Acme’”once. The crowd-sourced utterances are then transmitted via the speechinterface 12 of the user device 28 (or another user device) to the STTengine 14, which transcribes the crowd-sourced utterances and providesthem to the alias collection engine 18. The alias collection engine 18then populates the aliases cell 128 of the task item 120 with thecollected transcribed aliases and returns the updated task item to thealias generation engine 20. In some examples, in order for acrowd-sourced transcribed alias to populate the aliases cell 128, itmust have occurred at least a predefined minimum number of times.

For example, in response to receiving, for each of two aliases, at leasta predefined minimum number of responses to crowd-sourcingpronunciations for “2L8”, the alias collection engine 18 populates thealiases cell 128 of the task item 120 with “too late” and “too-el-ate”corresponding to two crowd-sourced pronunciation aliases. It should beappreciated that each alias can be represented in the aliases cell 128in more than one way, i.e., as alternative but equivalent or at leastsubstantially equivalent spellings to ensure that minor variations inspeech transcription by the STT engine 14 are nevertheless mapped to theappropriate alias.

It should be appreciated that aliases can be collected withoutcrowd-sourcing. For example, specific user accounts can be targeted toprovide aliases, or the artists themselves can be targeted to providealiases.

The alias collection engine 18 passes the updated task item 120 to thealias generation engine 20. In some examples, the alias generationengine 20 then classifies the content item and populates theclassification cell 126 with one or more tags corresponding to theclassification. In some examples, the alias generation engine 20classifies the content item based at least in part on the aliases thathave been collected. In some examples, the alias generation engine 20classifies the content item based at least in part on the name entity ofthe content item. In still further examples, the alias generation engine20 classifies the content item based at least in part on the aliasesthat have been collected and comparing the collected aliases from thecell 128 to the one or more name entities from the cell 124.

For example, the alias generation engine 20 compares the aliases “toolate” and “too-el-ate” to the name entity “2L8” and determines that themedia item should be classified as an “Alternative Spelling.”Non-limiting examples of classifications will be described below.

In some examples, the alias generation engine 20 uses theclassifications and/or aliases for one or more name entities togeneralize rules for determining aliases for other name entities withoutthe need for crowd-sourcing pronunciations. In some examples, the rulesare stored in a rules storage 74 (e.g., a database), as shown in FIG. 1,and accessed by the alias generation engine 20. The alias generationengine 20 uses the classifications and/or aliases in a machine learningmodel 23 to generate and apply alias generation rules for other nameentities and content items. For example, using 2L8's aliases andclassification, the alias generation engine 20 generates a rule storedin the rules storage 74 for determining a generalized alias for“Alternative Spelling” name entities in which a number in the nameentity is phonetically spelled out. The alias generation engine 20 canthen use the stored rule to generate one or more aliases for anothermedia item having a name entity classified as “Alternative Spelling” andcontaining a number. Thus, it should be appreciated that, in someexamples, the alias generation engine 20 generates one or more aliasesfor a detected under-locatable name entity without using the aliascollection engine 18 to collect aliases by crowd-sourcing or anothercollection means.

In some examples, the alias generation engine 20 and/or the retrievalengine 16 uses one or more rules to directly classify a particular nameentity and provide that classification to the table 50 independently ofgenerating or collecting any aliases for that name entity. For example,the retrieval engine 16 identifies a character in a name entity that isnot a letter, and the media item associated with that name entity iscorrespondingly tagged in the classification column 156 of the table 50.

From the updated task item 120 including the collected and/or generatedaliases, the table 50 in the name entity storage 24 is then updated toinclude the collected and/or generated aliases associated with thecontent item ID. Subsequently, when the system 10 is in playback mode atranscribed uttered playback request is compared by the retrieval engine16 to the name entity and any associated aliases of that name entitywhen identifying in the table 50 a content item ID corresponding to theplayback request. For example, a subsequent transcribed uttered requestto “play too late” is correctly mapped to the name entity 2L8 and itscorresponding content ID in the table 50 using the collected orgenerated alias “too late”.

An illustration of an example embodiment of the table 50 is illustratedin FIG. 4. The table 50 includes a content ID column 150, a name entitycolumn 152, and an alias column 154. Optionally, it also includes aclassification tag column 156. A content item ID is located by retrievalengine 16 by matching up the corresponding name entity or alias to atranscribed utterance in the table 50.

The following classifications of media content name entities arenon-limiting and for illustration purposes only. As mentioned, in someexamples, the alias generation engine 20 generates one or more aliasesfor a name entity at least in part based on the name entity'sclassification.

One example name entity classification is English dialects andneologisms. In this classification, a name entity uses one or more newwords that may contribute to a dialect or that are spelled in a wayintended to convey a certain dialect of English speech. An example isthe name entity “She Da Best”. The determiner “da” in “She Da Best” isspoken distinctly from the Standard American English equivalent “the”.Typical machine voice-driven retrieval systems can fail to correctlyform this dialect speech and often change it to Standard AmericanEnglish.

Another example name entity classification is abbreviations andambiguous acronyms. Abbreviations and ambiguous acronyms consist of nameentities that include shortened or abbreviated words in their titles ortextual cues that imply abbreviation or acronym. An example of a trueacronym name entity is “B.Y.O.B.”. Abbreviations are often ambiguous intheir pronunciation. An example of an ambiguous name entity is “BILD”.While “BILD” may be intended solely as an alternative spelling (for“build”), users may interpret the capitalization cues to imply that theyshould pronounce each letter individually.

Another example name entity is classification is numbers, dates, andtimes, examples being the name entity “2^(nd) Birthday”. Similar to theabbreviations class, there is a risk of users resorting to utteranceshaving multiple textual representations of the same spoken phrases. Forexample, “2^(nd) Birthday” could also be transcribed as “SecondBirthday”. Similarly, times can be represented in different ways, e.g.,‘seven hundred hours’ may be uttered instead of ‘Seven AM’ or ‘7o'clock’. Similarly, dates can be represented in different ways, e.g.,‘7/11’ could be ‘Seven eleven’, ‘July Eleventh’, or ‘November Seventh’.

Another example name entity is classification is removal of spaces.Removing spaces in name entities can present challenges for machinevoice-driven retrieval, such as in the name entity “TREEFALL”. Removingspaces can prevent a transcribed utterance from being mapped to theappropriate name entity.

Another example name entity classification is vocables. Vocables areutterances that are not words but do contain meaning. Commonly usedexamples in everyday language are ‘uh-huh’ to agree with something and‘ew’ to express disgust. Many lexicons of automatic speech recognitionsystems do not include many vocables, thereby causing a name entityincluding such a vocable to be under-locatable.

Another example name entity classification that can causeunder-locatability of that name entity is non-replacement symbols, i.e.,name entities containing one or symbols e.g., for conveying a specificfeeling, such as the name entity $Opportunity$.

Another example name entity classification that can causeunder-locatability of that name entity is orthographically andsemantically similar replacement symbols, such as name entities that usethe symbol “▴” instead of an “A”.

Another example name entity classification that can causeunder-locatability of that name entity is expressive and alternativespellings. Alternative spellings are not intended to modify thepronunciation of the word. For example, “Bild me up” is still intendedto be pronounced the same as “Build me up”. Alternative spellings maycause under-locatability because the actual name entity can besubstantially different from the transcription that the automatic speechrecognition system produces. Combinations of alternative spelling anddialects may be particularly challenging to locate.

Another example name entity classification that can causeunder-locatability of that name entity is wordplay, such as homophones,puns and portmanteau. For example, the artist “Knowmansland” may beunder-locatable by an automatic speech recognition system thattranscribes a corresponding playback request as “no man's land”.

Another example name entity classification that can causeunder-locatability of that name entity is proper nouns due to theplurality of spelling and pronunciation differences commonly used.

As described above, once a name entity has been classified the mediaitem associated with that name entity is tagged with the one or moreclassifications. For example, the task item 120 (FIG. 3) is updated withthe classification tag or tags, which is then used to update theclassification tag column 156 of the table 50 (FIG. 4).

Name entities that have been so tagged can inform generalizable aliasgeneration by the alias generation engine 20. In addition, name entitiesthat have been so tagged can inform the RIM 42 at least in part as towhether a particular media item is locatable or under-locatable. Forexample, if there is limited data from which the RIM 42 can calculate anutterance resolve frequency for a given media item but the data that isavailable suggests that the media item is under-locatable, the RIM 42can nevertheless determine that the media item is locatable if the nameentity associated with the media item does not fall within anypredefined classifications known to be associated withunder-locatability.

FIG. 5 schematically illustrates an example process flow 200 for thesystem 10 of FIG. 1.

Referring to the flow 200, at a block 202 a locatability detection for amedia item X is triggered. Non-limiting example triggering eventsinclude: a user-entered command to perform locatability detection on theparticular media item X; a preset periodic locatability detection of themedia item X; and a user feedback following a media item playbackrequest for the media item X or another media item.

The locability trigger causes, at block 204, a retrieval of playbackrecords specific to the media item X or a retrieval of a sufficientlyrecent playback records specific to the media item X.

At block 206, all of the retrieved playback records are counted,corresponding to all playback records specific to the media item Xregardless of retrieval modality.

At block 208, the number counted at block 206 is compared to apredefined threshold minimum number of playback records. If the countednumber is smaller than the predefined minimum then at block 210 the flow200 ends. If the counted number is at least as large as the predefinedminimum then at block 212, a number of counted playback recordscorresponding to retrievals by utterance is counted and compared to thenumber obtained at block 206 to obtain a resolvability value for themedia item X. In some examples, the resolvability value is normalizedaccording to a normalizing function.

At block 214, if the resolvability value is larger than a predefinedmaximum threshold number, the content entity is determined to belocatable and the process flow ends at block 216. If the resolvabilityvalue is not larger than the predefined maximum, then at block 218 it isdetermined if there is sufficient data about the media item to generatea pronunciation alias. Such data includes, for example, a classificationtag associated with the media item. The classification tag may bepre-associated with the media item, generated at block 218, or generatedat block 224 and used for a subsequent process flow for the same mediaitem.

If there is enough data to generate a pronunciation alias, at block 220a pronunciation alias is generated and associated with the media item.Optionally, the flow then proceeds to block 222 or skips to block 228.

If there is not enough data to generate a pronunciation alias, the flowproceeds from block 218 to block 222, where one or more pronunciationaliases for the media item are crowdsourced and associated with themedia item. From block 222, the flow 200 optionally proceeds to block224; otherwise it proceeds to block 228.

At block 224, the one or more crowd-sourced aliases are used to applyone or more classification tags to the media item.

From block 224, the flow 200 optionally proceeds to block 226;otherwise, it proceeds to block 228.

At block 226, one or more generalized alias generation rules are createdusing the one or more classification tags from block 224 and/or one ormore name entities already associated with the media item.

From block 226, the flow 200 proceeds to block 228. At block 228 themedia item is retrieved and played back using an utterance retrievalmodality that includes in the utterance a generated alias from block 220or a crowd-sourced alias from block 222.

It should be appreciated that blocks 202, 204, 206, 208, 210, 212, 214,216, 218, 220, 222, 224, and 226 correspond to a retrieval optimizationmode of the system 10 of FIG. 1, while block 228 corresponds to aplayback mode of the system 10. Thus, it should be appreciated thatretrieval optimization improves playback mode.

FIG. 5 is a block diagram showing an exemplary device 500 constructed torealize one or more aspects of the example embodiments described herein.In some examples, the device 500 corresponds to the user device 28. Inthese examples, the device 28 may be connected over the network 2 to oneor more servers 502 or other remote devices. The one or more servers 502can include one or more components described below in relation to thedevice 500, including a mass storage device and a processor device. Thatis, various operations and processes described herein can be performedby the cooperation of two or more devices, systems, processes, orcombinations thereof. Such a division of operations provides forefficient use of computing resources because servers are generally morepowerful than the user device 28.

The device 500 includes a processing device 510. Also included are amain memory 525 and an interconnect bus 505. The processor device 510may include without limitation a single microprocessor, or may include aplurality of microprocessors for configuring the device 500 for mediacontent locatability detection and improvement. The main memory 525stores, among other things, instructions and/or data for execution bythe processor device 510. The main memory 525 may include banks ofdynamic random access memory (DRAM), as well as cache memory.

The device 500 may further include a mass storage device 530, peripheraldevice(s) 540, audio input/output device(s) 542 (e.g., a microphone,speaker), portable non-transitory storage medium device(s) 550, inputcontrol device(s) 580, a media playback device 590 (e.g., a speaker), agraphics subsystem 560, and/or an output display interface 570. Forexplanatory purposes, all components in the device 500 are shown in FIG.6 as being coupled via the bus 505. However, the device 500 is not solimited. Elements of the device 500 may be coupled via one or more datatransport means. For example, the processor device 510, and/or the mainmemory 525 may be coupled via a local microprocessor bus. The massstorage device 530, peripheral device(s) 540, portable storage mediumdevice(s) 550, and/or graphics subsystem 560 may be coupled via one ormore input/output (I/O) buses. The mass storage device 530 may be anonvolatile storage device for storing data and/or instructions for useby the processor device 510. The mass storage device 530 can alsocorrespond to one or more of the media repository 22, the playbackhistory storage 30, the rules database 72, and the name entity storage24 described above. The mass storage device 530 may be implemented, forexample, with a magnetic disk drive or an optical disk drive. In asoftware embodiment, the mass storage device 530 is configured forloading contents of the mass storage device 530 into the main memory525. Memory may be embodied as one or more of mass storage device 530,main memory 525, or portable storage medium device 550.

Mass storage device 530 may additionally store the STT engine 14, theretrieval engine 16, the alias generation engine 20, and/or the aliascollection engine 18. The mass storage device 530 may also includesoftware that, when executed, causes the device 500 to perform thefeatures described above, including but not limited to the functions ofthe STT engine 14, the retrieval engine 16, the alias generation engine20, and/or the alias collection engine 18.

The portable storage medium device 550 operates in conjunction with anonvolatile portable storage medium, such as, for example, a solid statedrive (SSD), to input and output data and code to and from the device500. In some embodiments, the software for storing information may bestored on a portable storage medium, and may be inputted into the device500 via the portable storage medium device 550. The peripheral device(s)540 may include any type of computer support device, such as, forexample, an input/output (I/O) interface configured to add additionalfunctionality to the device 500. For example, the peripheral device(s)540 may include a network interface card for interfacing the device 500with a network 2. The audio input/output devices 542 may correspond tothe interfaces 12 and/or 26 and may include a microphone and/or speaker.

The input control device(s) 580 provide a portion of an interface forthe device 500. The input control device(s) 580 may include a keypadand/or a cursor control and/or a touch screen. The keypad may beconfigured for inputting alphanumeric characters and/or other keyinformation. The cursor control device may include, for example, ahandheld controller or mouse, a rotary input mechanism, a trackball, astylus, and/or cursor direction keys. In order to display textual andgraphical information, the device 500 may include the graphics subsystem560 and the output display 570. The output display 570 may include adisplay such as a TFT (Thin Film Transistor), TFD (Thin Film Diode),OLED (Organic Light-Emitting Diode), AMOLED display (active-matrixorganic light-emitting diode), and/or liquid crystal display (LCD)-typedisplays. The displays can also be touchscreen displays, such ascapacitive and resistive-type touchscreen displays.

The graphics subsystem 560 receives text-based and graphicalinformation, and processes the information for output to the outputdisplay 570, such as textual requests for uttered aliases.

Input control devices 580 can control the operation and variousfunctions of device 500. Input control devices 580 can include anycomponents, circuitry, or logic operative to drive the functionality ofdevice 500. For example, input control device(s) 580 can include one ormore processors acting under the control of an application.

Each component of the device 500 may represent a broad category of acomputer component of a general and/or special purpose computer.Components of the device 500 are not limited to the specificimplementations provided herein.

Software embodiments of the examples presented herein may be provided asa computer program product, or software that may include an article ofmanufacture on a machine-accessible or machine-readable media havinginstructions. The instructions on the non-transitory machine-accessiblemachine-readable or computer-readable medium may be used to program acomputer system or other electronic device. The machine- orcomputer-readable medium may include, but is not limited to, magneticdisks, optical disks, magneto-optical disks, or other types ofmedia/machine-readable medium suitable for storing or transmittingelectronic instructions. The techniques described herein are not limitedto any particular software configuration. They may find applicability inany computing or processing environment. The terms “computer-readable”,“machine-accessible medium” or “machine-readable medium” used hereinshall include any medium that is capable of storing, encoding, ortransmitting a sequence of instructions for execution by the machine,and which causes the machine to perform any one of the methods describedherein. Further, it is common in the art to speak of software, in oneform or another (e.g., program, procedure, process, application, module,engine, unit, logic, and so on), as taking an action or causing aresult. Such expressions are merely a shorthand way of stating that theexecution of the software by a processing system causes the processor toperform an action to produce a result.

Some embodiments may also be implemented by the preparation ofapplication-specific integrated circuits, field-programmable gatearrays, or by interconnecting an appropriate network of conventionalcomponent circuits.

Some embodiments include a computer program product. The computerprogram product may be a storage medium or media having instructionsstored thereon or therein that can be used to control, or cause, acomputer to perform any of the procedures of the example embodiments ofthe invention. The storage medium may include without limitation anoptical disc, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flashmemory, a flash card, a magnetic card, an optical card, nanosystems, amolecular memory integrated circuit, a RAID, remote datastorage/archive/warehousing, and/or any other type of device suitablefor storing instructions and/or data.

Stored on any one of the computer-readable medium or media, someimplementations include software for controlling both the hardware ofthe system and for enabling the system or microprocessor to interactwith a human user or other mechanism utilizing the results of theexample embodiments of the invention. Such software may include withoutlimitation device drivers, operating systems, and user applications.Ultimately, such computer-readable media further include software forperforming example aspects of the invention, as described above.

Included in the programming and/or software of the system are softwaremodules for implementing the procedures described above.

While various example embodiments of the present invention have beendescribed above, it should be understood that they have been presentedby way of example, and not limitation. It will be apparent to personsskilled in the relevant art(s) that various changes in form and detailcan be made therein. Thus, the present invention should not be limitedby any of the above described example embodiments, but should be definedonly in accordance with the following claims and their equivalents.Further, the Abstract is not intended to be limiting as to the scope ofthe example embodiments presented herein in any way. It is also to beunderstood that the procedures recited in the claims need not beperformed in the order presented.

1. A method for detecting an under-locatable media content itemcomprising: ascertaining a first number of playbacks of the mediacontent item; ascertaining a second number of playbacks of the mediacontent item, where each of the second number of playbacks correspondsto one of the first number of playbacks triggered by a machinevoice-driven retrieval of a playback command utterance; and comparingthe first number to the second number to generate: 1) a resolvabilityvalue for the media content item; or 2) an utterance resolve frequencyfor the media content item.
 2. The method of claim 1, wherein each ofthe first number of playbacks of the media content item corresponds to aplayback command provided by any of a plurality of command modalities,wherein one of the command modalities is natural speech and another ofthe command modalities is text.
 3. The method of claim 1, furthercomprising: 1) comparing the utterance resolve frequency to a predefinedthreshold frequency and, if the utterance resolve frequency is less thanthe predefined threshold frequency, determining that the media contentitem is under-locatable; or 2) comparing the resolvability value to apredefined threshold resolvability value and, if the resolvability valueis less than the predefined resolvability value, determine that themedia content item is under-locatable.
 4. The method of claim 1, whereinthe media content item is associated with one or more name entities, andthe method further comprises: classifying the one or more name entitieswith one or more tags, each of the one or more tags corresponding to aclass of name entities that are under-locatable by machine voice-drivenretrieval.
 5. The method of claim 4, further comprising: generating atleast one pronunciation alias for the one or more name entities; whereinthe generating is based at least partially on the one or more tags. 6.The method of claim 5, further comprising: locating the media contentitem using a machine voice-driven retrieval of an utterance containingthe at least one pronunciation alias; and playing back the located mediacontent item.
 7. The method of claim 1, further comprising: collectingat least one pronunciation alias for the one or more name entities; andassociating a transcription of the at least one pronunciation alias withthe media content item.
 8. The method of claim 7, further comprising:locating the media content item using a machine voice-driven retrievalof an utterance containing the at least one pronunciation alias; andplaying back the located media content item.
 9. A system for detectingan under-locatable media content item, comprising: one or moreprocessors adapted to: ascertain a first number of playbacks of themedia content item; ascertain a second number of playbacks of the mediacontent item, where each of the second number of playbacks correspondsto one of the first number of playbacks triggered by a machinevoice-driven retrieval of a playback command utterance; and compare thefirst number to the second number to generate: 1) a resolvability valuefor the media content item; or 2) an utterance resolve frequency for themedia content item.
 10. The system of claim 9, wherein each of the firstnumber of playbacks of the media content item corresponds to a playbackcommand provided by any of a plurality of command modalities, whereinone of the command modalities is natural speech and another of thecommand modalities is text.
 11. The system of claim 9, wherein the oneor more processors are further adapted to: 1) compare the utteranceresolve frequency to a predefined threshold frequency and, if theutterance resolve frequency is less than the predefined thresholdfrequency, determine that the media content item is under-locatable; or2) compare the resolvability value to a predefined thresholdresolvability value and, if the resolvability value is less than thepredefined threshold resolvability value, determine that the mediacontent item is under-locatable.
 12. The system of claim 9, wherein themedia content item is associated with one or more name entities, and theone or more processors are further adapted to: classify the one or morename entities with one or more tags, each of the one or more tagscorresponding to a class of name entities that are under-locatable bymachine voice-driven retrieval.
 13. The system of claim 12, wherein theone or more processors are further adapted to: generate at least onepronunciation alias for the one or more name entities; wherein thegenerate is based at least partially on the one or more tags.
 14. Thesystem of claim 12, wherein the one or more processors are furtheradapted to: locate the media content item using a machine voice-drivenretrieval of an utterance containing the at least one pronunciationalias; and play back the located media content item.
 15. The system ofclaim 9, wherein the one or more processors are further adapted to:collect at least one pronunciation alias for the one or more nameentities; and associate a transcription of the at least onepronunciation alias with the media content item.
 16. The system of claim15, wherein the one or more processors are further adapted to: locatethe media content item using a machine voice-driven retrieval of anutterance containing the at least one pronunciation alias; and play backthe located media content item.
 17. A non-transitory computer-readablemedium, comprising: one or more sequences of instructions that, whenexecuted by one or more processors, causes the one or more processors todetect an under-locatable media content item by: ascertaining a firstnumber of playbacks of the media content item; ascertaining a secondnumber of playbacks of the media content item, where each of the secondnumber of playbacks corresponds to one of the first number of playbackstriggered by a machine voice-driven retrieval of a playback commandutterance; and comparing the first number to the second number togenerate: 1) a resolvability value for the media content item; or 2) anutterance resolve frequency for the media content item.
 18. Thenon-transitory computer-readable medium of claim 17, wherein each of thefirst number of playbacks of the media content item corresponds to aplayback command provided by any of a plurality of command modalities,wherein one of the command modalities is natural speech and another ofthe command modalities is text.
 19. The non-transitory computer-readablemedium of claim 17, wherein the one or more sequences of instructions,when executed by the one or more processors, causes the one or moreprocessors to: 1) compare the utterance resolve frequency to apredefined threshold frequency and, if the utterance resolve frequencyis less than the predefined threshold frequency, determine that themedia content item is under-locatable; or 2) compare the resolvabilityvalue to a predefined threshold resolvability value and, if theresolvability value is less than the predefined threshold resolvabilityvalue, determine that the media content item is under-locatable.
 20. Thenon-transitory computer-readable medium of claim 17, wherein the mediacontent item is associated with one or more name entities, and whereinthe one or more sequences of instructions, when executed by the one ormore processors, causes the one or more processors to: classify the oneor more name entities with one or more tags, each of the one or moretags corresponding to a class of name entities that are under-locatableby machine voice-driven retrieval.
 21. The non-transitorycomputer-readable medium of claim 20, wherein the one or more sequencesof instructions, when executed by the one or more processors, causes theone or more processors to: generate at least one pronunciation alias forthe one or more name entities; wherein the generate is based at leastpartially on the one or more tags.
 22. The non-transitorycomputer-readable medium of claim 21, wherein the one or more sequencesof instructions, when executed by the one or more processors, causes theone or more processors to: locate the media content item using a machinevoice-driven retrieval of an utterance containing the at least onepronunciation alias; and play back the located media content item. 23.The non-transitory computer-readable medium of claim 17, wherein the oneor more sequences of instructions, when executed by the one or moreprocessors, causes the one or more processors to: collect at least onepronunciation alias for the one or more name entities; and associate atranscription of the at least one pronunciation alias with the mediacontent item.
 24. The non-transitory computer-readable medium of claim23, wherein the one or more sequences of instructions, when executed bythe one or more processors, causes the one or more processors to: locatethe media content item using a machine voice-driven retrieval of anutterance containing the at least one pronunciation alias; and play backthe located media content item.